{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Neural Networks Regression"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# fix_yahoo_finance is used to fetch data \n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2007-01-01'\n",
        "end = '2018-08-27'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 downloaded\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>3.85</td>\n",
              "      <td>3.98</td>\n",
              "      <td>3.84</td>\n",
              "      <td>3.95</td>\n",
              "      <td>3.95</td>\n",
              "      <td>20548400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>3.98</td>\n",
              "      <td>4.00</td>\n",
              "      <td>3.88</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>22887200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>4.01</td>\n",
              "      <td>4.18</td>\n",
              "      <td>3.99</td>\n",
              "      <td>4.13</td>\n",
              "      <td>4.13</td>\n",
              "      <td>42398300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
              "      <td>4.19</td>\n",
              "      <td>4.25</td>\n",
              "      <td>4.11</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>42932100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-08</th>\n",
              "      <td>4.23</td>\n",
              "      <td>4.26</td>\n",
              "      <td>4.14</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>30678700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Open  High   Low  Close  Adj Close    Volume\n",
              "Date                                                    \n",
              "2014-01-02  3.85  3.98  3.84   3.95       3.95  20548400\n",
              "2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200\n",
              "2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300\n",
              "2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100\n",
              "2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.reset_index()\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-01-02</td>\n",
              "      <td>3.85</td>\n",
              "      <td>3.98</td>\n",
              "      <td>3.84</td>\n",
              "      <td>3.95</td>\n",
              "      <td>3.95</td>\n",
              "      <td>20548400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-01-03</td>\n",
              "      <td>3.98</td>\n",
              "      <td>4.00</td>\n",
              "      <td>3.88</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>22887200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-01-06</td>\n",
              "      <td>4.01</td>\n",
              "      <td>4.18</td>\n",
              "      <td>3.99</td>\n",
              "      <td>4.13</td>\n",
              "      <td>4.13</td>\n",
              "      <td>42398300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-01-07</td>\n",
              "      <td>4.19</td>\n",
              "      <td>4.25</td>\n",
              "      <td>4.11</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>42932100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-01-08</td>\n",
              "      <td>4.23</td>\n",
              "      <td>4.26</td>\n",
              "      <td>4.14</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>30678700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date  Open  High   Low  Close  Adj Close    Volume\n",
              "0 2014-01-02  3.85  3.98  3.84   3.95       3.95  20548400\n",
              "1 2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200\n",
              "2 2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300\n",
              "3 2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100\n",
              "4 2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Increase_Decrease'] = np.where(dataset['Volume'].shift(-1) > dataset['Volume'],1,0)\n",
        "dataset['Buy_Sell_on_Open'] = np.where(dataset['Open'].shift(-1) > dataset['Open'],1,0)\n",
        "dataset['Buy_Sell'] = np.where(dataset['Adj Close'].shift(-1) > dataset['Adj Close'],1,0)\n",
        "dataset['Returns'] = dataset['Adj Close'].pct_change()\n",
        "dataset = dataset.dropna()"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Exploration"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Increase_Decrease</th>\n",
              "      <th>Buy_Sell_on_Open</th>\n",
              "      <th>Buy_Sell</th>\n",
              "      <th>Returns</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-01-06</td>\n",
              "      <td>4.01</td>\n",
              "      <td>4.18</td>\n",
              "      <td>3.99</td>\n",
              "      <td>4.13</td>\n",
              "      <td>4.13</td>\n",
              "      <td>42398300</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.032500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-01-07</td>\n",
              "      <td>4.19</td>\n",
              "      <td>4.25</td>\n",
              "      <td>4.11</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>42932100</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.012107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-01-08</td>\n",
              "      <td>4.23</td>\n",
              "      <td>4.26</td>\n",
              "      <td>4.14</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>30678700</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2014-01-09</td>\n",
              "      <td>4.20</td>\n",
              "      <td>4.23</td>\n",
              "      <td>4.05</td>\n",
              "      <td>4.09</td>\n",
              "      <td>4.09</td>\n",
              "      <td>30667600</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.021531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2014-01-10</td>\n",
              "      <td>4.09</td>\n",
              "      <td>4.20</td>\n",
              "      <td>4.07</td>\n",
              "      <td>4.17</td>\n",
              "      <td>4.17</td>\n",
              "      <td>20840800</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.019560</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date  Open  High   Low  Close  Adj Close    Volume  Increase_Decrease  \\\n",
              "2 2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300                  1   \n",
              "3 2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100                  0   \n",
              "4 2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700                  0   \n",
              "5 2014-01-09  4.20  4.23  4.05   4.09       4.09  30667600                  0   \n",
              "6 2014-01-10  4.09  4.20  4.07   4.17       4.17  20840800                  1   \n",
              "\n",
              "   Buy_Sell_on_Open  Buy_Sell   Returns  \n",
              "2                 1         1  0.032500  \n",
              "3                 1         0  0.012107  \n",
              "4                 0         0  0.000000  \n",
              "5                 0         1 -0.021531  \n",
              "6                 1         0  0.019560  "
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.tail()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Increase_Decrease</th>\n",
              "      <th>Buy_Sell_on_Open</th>\n",
              "      <th>Buy_Sell</th>\n",
              "      <th>Returns</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1167</th>\n",
              "      <td>2018-08-21</td>\n",
              "      <td>19.980000</td>\n",
              "      <td>20.420000</td>\n",
              "      <td>19.860001</td>\n",
              "      <td>20.400000</td>\n",
              "      <td>20.400000</td>\n",
              "      <td>55629000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.021021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1168</th>\n",
              "      <td>2018-08-22</td>\n",
              "      <td>20.280001</td>\n",
              "      <td>20.920000</td>\n",
              "      <td>20.209999</td>\n",
              "      <td>20.900000</td>\n",
              "      <td>20.900000</td>\n",
              "      <td>62002700</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.024510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1169</th>\n",
              "      <td>2018-08-23</td>\n",
              "      <td>21.190001</td>\n",
              "      <td>22.320000</td>\n",
              "      <td>21.139999</td>\n",
              "      <td>22.290001</td>\n",
              "      <td>22.290001</td>\n",
              "      <td>113444100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.066507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1170</th>\n",
              "      <td>2018-08-24</td>\n",
              "      <td>22.910000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>22.670000</td>\n",
              "      <td>23.980000</td>\n",
              "      <td>23.980000</td>\n",
              "      <td>164328200</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.075819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1171</th>\n",
              "      <td>2018-08-27</td>\n",
              "      <td>24.940001</td>\n",
              "      <td>27.299999</td>\n",
              "      <td>24.629999</td>\n",
              "      <td>25.260000</td>\n",
              "      <td>25.260000</td>\n",
              "      <td>325058400</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.053378</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Date       Open       High        Low      Close  Adj Close  \\\n",
              "1167 2018-08-21  19.980000  20.420000  19.860001  20.400000  20.400000   \n",
              "1168 2018-08-22  20.280001  20.920000  20.209999  20.900000  20.900000   \n",
              "1169 2018-08-23  21.190001  22.320000  21.139999  22.290001  22.290001   \n",
              "1170 2018-08-24  22.910000  24.000000  22.670000  23.980000  23.980000   \n",
              "1171 2018-08-27  24.940001  27.299999  24.629999  25.260000  25.260000   \n",
              "\n",
              "         Volume  Increase_Decrease  Buy_Sell_on_Open  Buy_Sell   Returns  \n",
              "1167   55629000                  1                 1         1  0.021021  \n",
              "1168   62002700                  1                 1         1  0.024510  \n",
              "1169  113444100                  1                 1         1  0.066507  \n",
              "1170  164328200                  1                 1         1  0.075819  \n",
              "1171  325058400                  0                 0         0  0.053378  "
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.dtypes"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": [
              "Date                 datetime64[ns]\n",
              "Open                        float64\n",
              "High                        float64\n",
              "Low                         float64\n",
              "Close                       float64\n",
              "Adj Close                   float64\n",
              "Volume                        int32\n",
              "Increase_Decrease             int32\n",
              "Buy_Sell_on_Open              int32\n",
              "Buy_Sell                      int32\n",
              "Returns                     float64\n",
              "dtype: object"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neural_network import MLPRegressor"
      ],
      "outputs": [],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.array(dataset['Returns']).reshape(1, -1)\n",
        "y = np.array(dataset['Adj Close']).reshape(1, -1)"
      ],
      "outputs": [],
      "execution_count": 39,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf = MLPRegressor(solver='lbfgs', alpha=1e-5,\n",
        "                hidden_layer_sizes=(5, 2), random_state=1)"
      ],
      "outputs": [],
      "execution_count": 40,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf.fit(X, y)    "
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 41,
          "data": {
            "text/plain": [
              "MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,\n",
              "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
              "       hidden_layer_sizes=(5, 2), learning_rate='constant',\n",
              "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
              "       nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,\n",
              "       solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
              "       warm_start=False)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 41,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cross_validation import train_test_split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size = 0.20, random_state = 0) "
      ],
      "outputs": [],
      "execution_count": 42,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf.predict(X_test)"
      ],
      "outputs": [],
      "execution_count": 43,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "ax1 = fig.add_subplot(111)\n",
        "ax1.scatter(X, y, s=10, c='b', marker=\"s\", label='real')\n",
        "ax1.scatter(X_test, Y_test, s=10, c='r', marker=\"o\", label='NN Prediction')\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": [
              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\n"
            ],
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 52,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf.coefs_"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 45,
          "data": {
            "text/plain": [
              "[array([[-0.01185904,  0.03148832, -0.07144261, -0.02825022, -0.05048491],\n",
              "        [-0.05826212, -0.04483904, -0.02207214, -0.01475378,  0.00554761],\n",
              "        [-0.01154855,  0.02647119, -0.04223907,  0.05403976, -0.06754478],\n",
              "        ...,\n",
              "        [ 0.02620193,  0.04208736, -0.00461847,  0.01820076,  0.05895923],\n",
              "        [-0.00614949, -0.05216219,  0.00617878, -0.01000024,  0.03834675],\n",
              "        [ 0.04254304, -0.04689909, -0.00886964, -0.03332546,  0.06498324]]),\n",
              " array([[-0.44611385, -0.03116298],\n",
              "        [ 0.72234035,  0.33617285],\n",
              "        [ 0.11688762,  0.69761271],\n",
              "        [-0.41425564,  0.91998276],\n",
              "        [-0.55578863,  0.62917536]]),\n",
              " array([[ 0.01215177, -0.03829643, -0.00235831, ...,  0.05126444,\n",
              "          0.01010033,  0.05572331],\n",
              "        [-0.03899801,  0.04206873, -0.06669257, ..., -0.03704846,\n",
              "         -0.06108063,  0.01160819]])]"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 45,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf.coefs_[0]"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 46,
          "data": {
            "text/plain": [
              "array([[-0.01185904,  0.03148832, -0.07144261, -0.02825022, -0.05048491],\n",
              "       [-0.05826212, -0.04483904, -0.02207214, -0.01475378,  0.00554761],\n",
              "       [-0.01154855,  0.02647119, -0.04223907,  0.05403976, -0.06754478],\n",
              "       ...,\n",
              "       [ 0.02620193,  0.04208736, -0.00461847,  0.01820076,  0.05895923],\n",
              "       [-0.00614949, -0.05216219,  0.00617878, -0.01000024,  0.03834675],\n",
              "       [ 0.04254304, -0.04689909, -0.00886964, -0.03332546,  0.06498324]])"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 46,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf.intercepts_[0]"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 47,
          "data": {
            "text/plain": [
              "array([ 0.06243686, -0.00938274, -0.00985981,  0.035226  ,  0.01371668])"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 47,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf.score(X, y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 48,
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 48,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "nbconvert_exporter": "python",
      "file_extension": ".py",
      "pygments_lexer": "ipython3",
      "name": "python",
      "mimetype": "text/x-python",
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      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      }
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "nteract": {
      "version": "0.12.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}